/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// Unit test for TFLite LSTM op.

#include <initializer_list>
#include <vector>

#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "flatbuffers/flatbuffers.h"  // from @flatbuffers
#include "tensorflow/lite/delegates/gpu/delegate.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/test_util.h"
#include "tensorflow/lite/schema/schema_generated.h"

namespace tflite {
namespace {

using ::testing::ElementsAreArray;

class LSTMOpModel : public SingleOpModel {
 public:
  LSTMOpModel(int n_batch, int n_input, int n_cell, int n_output, bool use_cifg,
              bool use_peephole, bool use_projection_weights,
              bool use_projection_bias, const TensorType weight_type,
              bool model_has_legacy_20_inputs, bool is_layer_norm,
              bool asymmetric_quantize_inputs,
              std::initializer_list<float> input_to_input_weights,
              std::initializer_list<float> input_to_forget_weights,
              std::initializer_list<float> input_to_cell_weights,
              std::initializer_list<float> input_to_output_weights,
              std::initializer_list<float> recurrent_to_input_weights,
              std::initializer_list<float> recurrent_to_forget_weights,
              std::initializer_list<float> recurrent_to_cell_weights,
              std::initializer_list<float> recurrent_to_output_weights,
              std::initializer_list<float> cell_to_input_weights,
              std::initializer_list<float> cell_to_forget_weights,
              std::initializer_list<float> cell_to_output_weights,
              std::initializer_list<float> input_gate_bias,
              std::initializer_list<float> forget_gate_bias,
              std::initializer_list<float> cell_gate_bias,
              std::initializer_list<float> output_gate_bias,
              std::initializer_list<float> projection_weights,
              std::initializer_list<float> projection_bias,
              std::initializer_list<float> input_layer_norm_coefficients,
              std::initializer_list<float> forget_layer_norm_coefficients,
              std::initializer_list<float> cell_layer_norm_coefficients,
              std::initializer_list<float> output_layer_norm_coefficients)
      : n_input_(n_input),
        n_output_(n_output),
        n_batch_(n_batch),
        weight_type_(weight_type) {
    input_ = AddInput({TensorType_FLOAT32, {n_batch, n_input}});

    if (use_cifg) {
      AddNullInput();
    } else {
      AddConstInput({weight_type, {n_cell, n_input}}, input_to_input_weights);
    }
    AddConstInput({weight_type, {n_cell, n_input}}, input_to_forget_weights);
    AddConstInput({weight_type, {n_cell, n_input}}, input_to_cell_weights);
    AddConstInput({weight_type, {n_cell, n_input}}, input_to_output_weights);

    if (use_cifg) {
      AddNullInput();
    } else {
      AddConstInput({weight_type, {n_cell, n_output}},
                    recurrent_to_input_weights);
    }
    AddConstInput({weight_type, {n_cell, n_output}},
                  recurrent_to_forget_weights);
    AddConstInput({weight_type, {n_cell, n_output}}, recurrent_to_cell_weights);
    AddConstInput({weight_type, {n_cell, n_output}},
                  recurrent_to_output_weights);

    if (use_peephole) {
      if (use_cifg) {
        AddNullInput();
      } else {
        AddConstInput({weight_type, {n_cell}}, cell_to_input_weights);
      }
      AddConstInput({weight_type, {n_cell}}, cell_to_forget_weights);
      AddConstInput({weight_type, {n_cell}}, cell_to_output_weights);
    } else {
      AddNullInput();
      AddNullInput();
      AddNullInput();
    }

    if (use_cifg) {
      AddNullInput();
    } else {
      AddConstInput({TensorType_FLOAT32, {n_cell}}, input_gate_bias);
    }
    AddConstInput({TensorType_FLOAT32, {n_cell}}, forget_gate_bias);
    AddConstInput({TensorType_FLOAT32, {n_cell}}, cell_gate_bias);
    AddConstInput({TensorType_FLOAT32, {n_cell}}, output_gate_bias);

    if (use_projection_weights) {
      AddConstInput({weight_type, {n_output, n_cell}}, projection_weights);
    } else {
      AddNullInput();
    }
    if (use_projection_bias) {
      CHECK(use_projection_weights);
      AddConstInput({TensorType_FLOAT32, {n_output}}, projection_bias);
    } else {
      AddNullInput();
    }

    // Adding the 2 state tensors.
    AddVariableInput({TensorType_FLOAT32, {n_batch, n_output}});
    AddVariableInput({TensorType_FLOAT32, {n_batch, n_cell}});

    // Layer norm weights.
    if (!model_has_legacy_20_inputs) {
      if (is_layer_norm) {
        if (use_cifg) {
          AddNullInput();
        } else {
          AddConstInput({TensorType_FLOAT32, {n_cell}},
                        input_layer_norm_coefficients);
        }
        AddConstInput({TensorType_FLOAT32, {n_cell}},
                      forget_layer_norm_coefficients);
        AddConstInput({TensorType_FLOAT32, {n_cell}},
                      cell_layer_norm_coefficients);
        AddConstInput({TensorType_FLOAT32, {n_cell}},
                      output_layer_norm_coefficients);
      } else {
        AddNullInput();
        AddNullInput();
        AddNullInput();
        AddNullInput();
      }
    }

    output_ = AddOutput({TensorType_FLOAT32, {n_batch, n_output}});

    // TODO(b/161825581): Add tests where cell_clip and/or proj_clip is not the
    // default 0.
    SetBuiltinOp(
        BuiltinOperator_LSTM, BuiltinOptions_LSTMOptions,
        CreateLSTMOptions(builder_, ActivationFunctionType_TANH,
                          /*cell_clip=*/0.0f, /*proj_clip=*/0.0f,
                          LSTMKernelType_FULL, asymmetric_quantize_inputs)
            .Union());

    // Input shapes are already set up, no need to pass them again.
    BuildInterpreter(/*input_shapes=*/{}, /*num_threads=*/-1,
                     /*allow_fp32_relax_to_fp16=*/false,
                     /*apply_delegate=*/false);

    auto options = TfLiteGpuDelegateOptionsV2Default();
    // MeanStddevNormalization is only implemented in OpenCL now.
    options.experimental_flags |= TFLITE_GPU_EXPERIMENTAL_FLAGS_CL_ONLY;
    SetDelegate(TfLiteGpuDelegateV2Create(&options));
  }

  ~LSTMOpModel() { EXPECT_EQ(CountOpsExecutedByCpuKernel(), 0); }

  void SetInput(int offset, const float* begin, const float* end) {
    SingleOpModel::PopulateTensor(input_, offset, const_cast<float*>(begin),
                                  const_cast<float*>(end));
  }

  std::vector<float> GetOutput() { return ExtractVector<float>(output_); }

  int num_inputs() { return n_input_; }
  int num_outputs() { return n_output_; }
  int num_batches() { return n_batch_; }

 protected:
  int input_;
  int output_;

  int n_input_;
  int n_output_;
  int n_batch_;

 private:
  const TensorType weight_type_;
};

// GetParam() => model_has_legacy_20_inputs
class LstmOpTest : public ::testing::TestWithParam<bool> {
 protected:
  // Weights of the LSTM model. Some are optional.
  std::initializer_list<float> input_to_input_weights_;
  std::initializer_list<float> input_to_forget_weights_;
  std::initializer_list<float> input_to_cell_weights_;
  std::initializer_list<float> input_to_output_weights_;
  std::initializer_list<float> recurrent_to_input_weights_;
  std::initializer_list<float> recurrent_to_forget_weights_;
  std::initializer_list<float> recurrent_to_cell_weights_;
  std::initializer_list<float> recurrent_to_output_weights_;
  std::initializer_list<float> cell_to_input_weights_;
  std::initializer_list<float> cell_to_forget_weights_;
  std::initializer_list<float> cell_to_output_weights_;
  std::initializer_list<float> input_gate_bias_;
  std::initializer_list<float> forget_gate_bias_;
  std::initializer_list<float> cell_gate_bias_;
  std::initializer_list<float> output_gate_bias_;
  std::initializer_list<float> projection_weights_;
  std::initializer_list<float> input_layer_norm_coefficients_;
  std::initializer_list<float> forget_layer_norm_coefficients_;
  std::initializer_list<float> cell_layer_norm_coefficients_;
  std::initializer_list<float> output_layer_norm_coefficients_;

  // LSTM input is stored as num_steps * num_batch * num_inputs vector.
  std::vector<std::vector<std::vector<float>>> lstm_input_;
  // LSTM output is stored as num_steps * num_batch * num_outputs vector.
  std::vector<std::vector<std::vector<float>>> lstm_golden_output_;

  // Compares output up to tolerance to the result of the lstm given the input.
  void VerifyGoldens(LSTMOpModel* lstm, float tolerance) {
    EXPECT_EQ(lstm->ApplyDelegate(), kTfLiteOk);

    const int num_inputs = lstm->num_inputs();
    const int num_outputs = lstm->num_outputs();
    const int num_batches = lstm->num_batches();

    ASSERT_EQ(lstm_input_.size(), lstm_golden_output_.size());
    const int num_steps = lstm_input_.size();

    for (int i = 0; i < num_steps; ++i) {
      ASSERT_EQ(num_batches, lstm_input_[i].size());
      for (int b = 0; b < num_batches; ++b) {
        ASSERT_EQ(num_inputs, lstm_input_[i][b].size());
        const float* batch_start = lstm_input_[i][b].data();
        const float* batch_end = batch_start + num_inputs;
        lstm->SetInput(b * num_inputs, batch_start, batch_end);
      }

      ASSERT_EQ(lstm->Invoke(), kTfLiteOk);

      std::vector<float> expected;
      ASSERT_EQ(num_batches, lstm_golden_output_[i].size());
      for (int b = 0; b < num_batches; ++b) {
        ASSERT_EQ(num_outputs, lstm_golden_output_[i][b].size());
        const float* batch_start = lstm_golden_output_[i][b].data();
        const float* batch_end = batch_start + num_outputs;
        expected.insert(expected.end(), batch_start, batch_end);
      }

      EXPECT_THAT(lstm->GetOutput(),
                  ElementsAreArray(ArrayFloatNear(expected, tolerance)));
    }
  }
};

TEST_P(LstmOpTest, NoCifg_NoPeephole_NoProjection_NoLayerNorm) {
  const int n_batch = 1;
  const int n_input = 2;
  // n_cell and n_output have the same size when there is no projection.
  const int n_cell = 4;
  const int n_output = 4;

  bool model_has_legacy_20_inputs = GetParam();

  input_to_input_weights_ = {-0.45018822, -0.02338299, -0.0870589,  -0.34550029,
                             0.04266912,  -0.15680569, -0.34856534, 0.43890524};
  input_to_cell_weights_ = {-0.50013041, 0.1370284,  0.11810488, 0.2013163,
                            -0.20583314, 0.44344562, 0.22077113, -0.29909778};
  input_to_forget_weights_ = {0.09701663,  0.20334584,  -0.50592935,
                              -0.31343272, -0.40032279, 0.44781327,
                              0.01387155,  -0.35593212};
  input_to_output_weights_ = {-0.25065863, -0.28290087, 0.04613829, 0.40525138,
                              0.44272184,  0.03897077,  -0.1556896, 0.19487578};
  input_gate_bias_ = {0., 0., 0., 0.};
  cell_gate_bias_ = {0., 0., 0., 0.};
  forget_gate_bias_ = {1., 1., 1., 1.};
  output_gate_bias_ = {0., 0., 0., 0.};

  recurrent_to_input_weights_ = {
      -0.0063535,  -0.2042388,  0.31454784,  -0.35746509,
      0.28902304,  0.08183324,  -0.16555229, 0.02286911,
      -0.13566875, 0.03034258,  0.48091322,  -0.12528998,
      0.24077177,  -0.51332325, -0.33502164, 0.10629296};

  recurrent_to_cell_weights_ = {
      -0.3407414,  0.24443203,  -0.2078532,  0.26320225,
      0.05695659,  -0.00123841, -0.4744786,  -0.35869038,
      -0.06418842, -0.13502428, -0.501764,   0.22830659,
      -0.46367589, 0.26016325,  -0.03894562, -0.16368064};

  recurrent_to_forget_weights_ = {
      -0.48684245, -0.06655136, 0.42224967,  0.2112639,
      0.27654213,  0.20864892,  -0.07646349, 0.45877004,
      0.00141793,  -0.14609534, 0.36447752,  0.09196436,
      0.28053468,  0.01560611,  -0.20127171, -0.01140004};

  recurrent_to_output_weights_ = {
      0.43385774,  -0.17194885, 0.2718237,  0.09215671,
      0.24107647,  -0.39835793, 0.18212086, 0.01301402,
      0.48572797,  -0.50656658, 0.20047462, -0.20607421,
      -0.51818722, -0.15390486, 0.0468148,  0.39922136};

  // num_steps * num_batch * num_inputs
  lstm_input_ = {{{2., 3.}}, {{3., 4.}}, {{1., 1.}}};
  // num_steps * num_batch * num_outputs
  lstm_golden_output_ = {{{-0.02973187, 0.1229473, 0.20885126, -0.15358765}},
                         {{-0.03716109, 0.12507336, 0.41193449, -0.20860538}},
                         {{-0.15053082, 0.09120187, 0.24278517, -0.12222792}}};

  LSTMOpModel lstm(
      n_batch, n_input, n_cell, n_output,
      /*use_cifg=*/false, /*use_peephole=*/false,
      /*use_projection_weights=*/false,
      /*use_projection_bias=*/false, /*weight_type=*/TensorType_FLOAT32,
      model_has_legacy_20_inputs,
      /*is_layer_norm=*/false, /*asymmetric_quantize_inputs=*/false,
      input_to_input_weights_, input_to_forget_weights_, input_to_cell_weights_,
      input_to_output_weights_, recurrent_to_input_weights_,
      recurrent_to_forget_weights_, recurrent_to_cell_weights_,
      recurrent_to_output_weights_, cell_to_input_weights_,
      cell_to_forget_weights_, cell_to_output_weights_, input_gate_bias_,
      forget_gate_bias_, cell_gate_bias_, output_gate_bias_,
      projection_weights_, {}, input_layer_norm_coefficients_,
      forget_layer_norm_coefficients_, cell_layer_norm_coefficients_,
      output_layer_norm_coefficients_);

  VerifyGoldens(&lstm, 0.00001f);
}

TEST_P(LstmOpTest, Cifg_Peephole_NoProjection_NoLayerNorm) {
  const int n_batch = 1;
  const int n_input = 2;
  // n_cell and n_output have the same size when there is no projection.
  const int n_cell = 4;
  const int n_output = 4;

  bool model_has_legacy_20_inputs = GetParam();

  input_to_cell_weights_ = {-0.49770179, -0.27711356, -0.09624726, 0.05100781,
                            0.04717243,  0.48944736,  -0.38535351, -0.17212132};

  input_to_forget_weights_ = {-0.55291498, -0.42866567, 0.13056988, -0.3633365,
                              -0.22755712, 0.28253698,  0.24407166, 0.33826375};

  input_to_output_weights_ = {0.10725588,  -0.02335852, -0.55932593,
                              -0.09426838, -0.44257352, 0.54939759,
                              0.01533556,  0.42751634};
  cell_gate_bias_ = {0., 0., 0., 0.};
  forget_gate_bias_ = {1., 1., 1., 1.};
  output_gate_bias_ = {0., 0., 0., 0.};

  recurrent_to_cell_weights_ = {
      0.54066205,  -0.32668582, -0.43562764, -0.56094903,
      0.42957711,  0.01841056,  -0.32764608, -0.33027974,
      -0.10826075, 0.20675004,  0.19069612,  -0.03026325,
      -0.54532051, 0.33003211,  0.44901288,  0.21193194};

  recurrent_to_forget_weights_ = {
      -0.13832897, -0.0515101,  -0.2359007, -0.16661474,
      -0.14340827, 0.36986142,  0.23414481, 0.55899,
      0.10798943,  -0.41174671, 0.17751795, -0.34484994,
      -0.35874045, -0.11352962, 0.27268326, 0.54058349};

  recurrent_to_output_weights_ = {
      0.41613156, 0.42610586,  -0.16495961, -0.5663873,
      0.30579174, -0.05115908, -0.33941799, 0.23364776,
      0.11178309, 0.09481031,  -0.26424935, 0.46261835,
      0.50248802, 0.26114327,  -0.43736315, 0.33149987};

  cell_to_forget_weights_ = {0.47485286, -0.51955009, -0.24458408, 0.31544167};
  cell_to_output_weights_ = {-0.17135078, 0.82760304, 0.85573703, -0.77109635};

  lstm_input_ = {{{2., 3.}}, {{3., 4.}}, {{1., 1.}}};
  lstm_golden_output_ = {{{-0.36444446, -0.00352185, 0.12886585, -0.05163646}},
                         {{-0.42312205, -0.01218222, 0.24201041, -0.08124574}},
                         {{-0.358325, -0.04621704, 0.21641694, -0.06471302}}};

  LSTMOpModel lstm(
      n_batch, n_input, n_cell, n_output,
      /*use_cifg=*/true, /*use_peephole=*/true,
      /*use_projection_weights=*/false,
      /*use_projection_bias=*/false, /*weight_type=*/TensorType_FLOAT32,
      model_has_legacy_20_inputs,
      /*is_layer_norm=*/false, /*asymmetric_quantize_inputs=*/false,
      input_to_input_weights_, input_to_forget_weights_, input_to_cell_weights_,
      input_to_output_weights_, recurrent_to_input_weights_,
      recurrent_to_forget_weights_, recurrent_to_cell_weights_,
      recurrent_to_output_weights_, cell_to_input_weights_,
      cell_to_forget_weights_, cell_to_output_weights_, input_gate_bias_,
      forget_gate_bias_, cell_gate_bias_, output_gate_bias_,
      projection_weights_, {}, input_layer_norm_coefficients_,
      forget_layer_norm_coefficients_, cell_layer_norm_coefficients_,
      output_layer_norm_coefficients_);

  VerifyGoldens(&lstm, 0.00001f);
}

TEST_P(LstmOpTest, NoCifg_Peephole_Projection_NoLayerNorm) {
  const int n_batch = 1;
  const int n_input = 5;
  const int n_cell = 20;
  const int n_output = 16;

  bool model_has_legacy_20_inputs = GetParam();

  input_to_input_weights_ = {
      0.021393683,  0.06124551,    0.046905167,  -0.014657677,  -0.03149463,
      0.09171803,   0.14647801,    0.10797193,   -0.0057968358, 0.0019193048,
      -0.2726754,   0.10154029,    -0.018539885, 0.080349885,   -0.10262385,
      -0.022599787, -0.09121155,   -0.008675967, -0.045206103,  -0.0821282,
      -0.008045952, 0.015478081,   0.055217247,  0.038719587,   0.044153627,
      -0.06453243,  0.05031825,    -0.046935108, -0.008164439,  0.014574226,
      -0.1671009,   -0.15519552,   -0.16819797,  -0.13971269,   -0.11953059,
      0.25005487,   -0.22790983,   0.009855087,  -0.028140958,  -0.11200698,
      0.11295408,   -0.0035217577, 0.054485075,  0.05184695,    0.064711206,
      0.10989193,   0.11674786,    0.03490607,   0.07727357,    0.11390585,
      -0.1863375,   -0.1034451,    -0.13945189,  -0.049401227,  -0.18767063,
      0.042483903,  0.14233552,    0.13832581,   0.18350165,    0.14545603,
      -0.028545704, 0.024939531,   0.050929718,  0.0076203286,  -0.0029723682,
      -0.042484224, -0.11827596,   -0.09171104,  -0.10808628,   -0.16327988,
      -0.2273378,   -0.0993647,    -0.017155107, 0.0023917493,  0.049272764,
      0.0038534778, 0.054764505,   0.089753784,  0.06947234,    0.08014476,
      -0.04544234,  -0.0497073,    -0.07135631,  -0.048929106,  -0.004042012,
      -0.009284026, 0.018042054,   0.0036860977, -0.07427302,   -0.11434604,
      -0.018995456, 0.031487543,   0.012834908,  0.019977754,   0.044256654,
      -0.39292613,  -0.18519334,   -0.11651281,  -0.06809892,   0.011373677};

  input_to_forget_weights_ = {
      -0.0018401089, -0.004852237,  0.03698424,   0.014181704,   0.028273236,
      -0.016726194,  -0.05249759,   -0.10204261,  0.00861066,    -0.040979505,
      -0.009899187,  0.01923892,    -0.028177269, -0.08535103,   -0.14585495,
      0.10662567,    -0.01909731,   -0.017883534, -0.0047269356, -0.045103323,
      0.0030784295,  0.076784775,   0.07463696,   0.094531395,   0.0814421,
      -0.12257899,   -0.033945758,  -0.031303465, 0.045630626,   0.06843887,
      -0.13492945,   -0.012480007,  -0.0811829,   -0.07224499,   -0.09628791,
      0.045100946,   0.0012300825,  0.013964662,  0.099372394,   0.02543059,
      0.06958324,    0.034257296,   0.0482646,    0.06267997,    0.052625068,
      0.12784666,    0.07077897,    0.025725935,  0.04165009,    0.07241905,
      0.018668644,   -0.037377294,  -0.06277783,  -0.08833636,   -0.040120605,
      -0.011405586,  -0.007808335,  -0.010301386, -0.005102167,  0.027717464,
      0.05483423,    0.11449111,    0.11289652,   0.10939839,    0.13396506,
      -0.08402166,   -0.01901462,   -0.044678304, -0.07720565,   0.014350063,
      -0.11757958,   -0.0652038,    -0.08185733,  -0.076754324,  -0.092614375,
      0.10405491,    0.052960336,   0.035755895,  0.035839386,   -0.012540553,
      0.036881298,   0.02913376,    0.03420159,   0.05448447,    -0.054523353,
      0.02582715,    0.02327355,    -0.011857179, -0.0011980024, -0.034641717,
      -0.026125094,  -0.17582615,   -0.15923657,  -0.27486774,   -0.0006143371,
      0.0001771948,  -8.470171e-05, 0.02651807,   0.045790765,   0.06956496};

  input_to_cell_weights_ = {
      -0.04580283,  -0.09549462,   -0.032418985,  -0.06454633,   -0.043528453,
      0.043018587,  -0.049152344,  -0.12418144,   -0.078985475,  -0.07596889,
      0.019484362,  -0.11434962,   -0.0074034138, -0.06314844,   -0.092981495,
      0.0062155537, -0.025034338,  -0.0028890965, 0.048929527,   0.06235075,
      0.10665918,   -0.032036792,  -0.08505916,   -0.10843358,   -0.13002433,
      -0.036816437, -0.02130134,   -0.016518239,  0.0047691227,  -0.0025825808,
      0.066017866,  0.029991534,   -0.10652836,   -0.1037554,    -0.13056071,
      -0.03266643,  -0.033702414,  -0.006473424,  -0.04611692,   0.014419339,
      -0.025174323, 0.0396852,     0.081777506,   0.06157468,    0.10210095,
      -0.009658194, 0.046511717,   0.03603906,    0.0069369148,  0.015960095,
      -0.06507666,  0.09551598,    0.053568836,   0.06408714,    0.12835667,
      -0.008714329, -0.20211966,   -0.12093674,   0.029450472,   0.2849013,
      -0.029227901, 0.1164364,     -0.08560263,   0.09941786,    -0.036999565,
      -0.028842626, -0.0033637602, -0.017012902,  -0.09720865,   -0.11193351,
      -0.029155117, -0.017936034,  -0.009768936,  -0.04223324,   -0.036159635,
      0.06505112,   -0.021742892,  -0.023377212,  -0.07221364,   -0.06430552,
      0.05453865,   0.091149814,   0.06387331,    0.007518393,   0.055960953,
      0.069779344,  0.046411168,   0.10509911,    0.07463894,    0.0075130584,
      0.012850982,  0.04555431,    0.056955688,   0.06555285,    0.050801456,
      -0.009862683, 0.00826772,    -0.026555609,  -0.0073611983, -0.0014897042};

  input_to_output_weights_ = {
      -0.0998932,   -0.07201956,  -0.052803773,  -0.15629593,  -0.15001918,
      -0.07650751,  0.02359855,   -0.075155355,  -0.08037709,  -0.15093534,
      0.029517552,  -0.04751393,  0.010350531,   -0.02664851,  -0.016839722,
      -0.023121163, 0.0077019283, 0.012851257,   -0.05040649,  -0.0129761,
      -0.021737747, -0.038305793, -0.06870586,   -0.01481247,  -0.001285394,
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      -0.027833903, 0.029774971,  0.1130802,     0.09218906,   0.09506135,
      -0.086665764, -0.037162706, -0.038880914,  -0.035832845, -0.014481564,
      -0.09825003,  -0.12048569,  -0.097665586,  -0.05287633,  -0.0964047,
      -0.11366429,  0.035777505,  0.13568819,    0.052451383,  0.050649304,
      0.05798951,   -0.021852335, -0.099848844,  0.014740475,  -0.078897946,
      0.04974699,   0.014160473,  0.06973932,    0.04964942,   0.033364646,
      0.08190124,   0.025535367,  0.050893165,   0.048514254,  0.06945813,
      -0.078907564, -0.06707616,  -0.11844508,   -0.09986688,  -0.07509403,
      0.06263226,   0.14925587,   0.20188436,    0.12098451,   0.14639415,
      0.0015017595, -0.014267382, -0.03417257,   0.012711468,  0.0028300495,
      -0.024758482, -0.05098548,  -0.0821182,    0.014225672,  0.021544158,
      0.08949725,   0.07505268,   -0.0020780868, 0.04908258,   0.06476295,
      -0.022907063, 0.027562456,  0.040185735,   0.019567577,  -0.015598739,
      -0.049097303, -0.017121866, -0.083368234,  -0.02332002,  -0.0840956};

  input_gate_bias_ = {0.02234832,   0.14757581,  0.18176508,  0.10380666,
                      0.053110216,  -0.06928846, -0.13942584, -0.11816189,
                      0.19483899,   0.03652339,  -0.10250295, 0.036714908,
                      -0.18426876,  0.036065217, 0.21810818,  0.02383196,
                      -0.043370757, 0.08690144,  -0.04444982, 0.00030581196};

  forget_gate_bias_ = {0.035185695, -0.042891346, -0.03032477, 0.23027696,
                       0.11098921,  0.15378423,   0.09263801,  0.09790885,
                       0.09508917,  0.061199076,  0.07665568,  -0.015443159,
                       -0.03499149, 0.046190713,  0.08895977,  0.10899629,
                       0.40694186,  0.06030037,   0.012413437, -0.06108739};

  cell_gate_bias_ = {-0.024379363, 0.0055531194, 0.23377132,   0.033463873,
                     -0.1483596,   -0.10639995,  -0.091433935, 0.058573797,
                     -0.06809782,  -0.07889636,  -0.043246906, -0.09829136,
                     -0.4279842,   0.034901652,  0.18797937,   0.0075234566,
                     0.016178843,  0.1749513,    0.13975595,   0.92058027};

  output_gate_bias_ = {0.046159424, -0.0012809046, 0.03563469,   0.12648113,
                       0.027195795, 0.35373217,    -0.018957434, 0.008907322,
                       -0.0762701,  0.12018895,    0.04216877,   0.0022856654,
                       0.040952638, 0.3147856,     0.08225149,   -0.057416286,
                       -0.14995944, -0.008040261,  0.13208859,   0.029760877};

  recurrent_to_input_weights_ = {
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  recurrent_to_cell_weights_ = {
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  recurrent_to_forget_weights_ = {
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      0.04299654,    -0.036394123,  -0.12915532,   0.09793732,   0.07512415,
      -0.11319543,   -0.032502122,  0.15661901,    0.07671967,   -0.005491124,
      -0.19379048,   -0.218606,     0.21448623,    0.017840758,  0.1416943,
      -0.07051762,   0.19488361,    0.02664691,    -0.18104725,  -0.09334311,
      0.15026465,    -0.15493552,   -0.057762887,  -0.11604192,  -0.262013,
      -0.01391798,   0.012185008,   0.11156489,    -0.07483202,  0.06693364,
      -0.26151478,   0.046425626,   0.036540434,   -0.16435726,  0.17338543,
      -0.21401681,   -0.11385144,   -0.08283257,   -0.069031075, 0.030635102,
      0.010969227,   0.11109743,    0.010919218,   0.027526086,  0.13519906,
      0.01891392,    -0.046839405,  -0.040167913,  0.017953383,  -0.09700955,
      0.0061885654,  -0.07000971,   0.026893595,   -0.038844477, 0.14543656};

  lstm_input_ = {// Step 1
                 {{0.787926, 0.151646, 0.071352, 0.118426, 0.458058}},
                 // Step 2
                 {{0.596268, 0.998386, 0.568695, 0.864524, 0.571277}},
                 // Step 3
                 {{0.073204, 0.296072, 0.743333, 0.069199, 0.045348}},
                 // Step 4
                 {{0.867394, 0.291279, 0.013714, 0.482521, 0.626339}}};

  lstm_golden_output_ = {
      {{-0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, -0.0211779,
        0.0283512, -0.0114597, 0.00907307, -0.0244004, -0.0152191, -0.0259063,
        0.00914318, 0.00415118, 0.017147, 0.0134203}},

      {{-0.0166936, 0.0381209, 0.000889694, 0.0143363, -0.0328911, -0.0234288,
        0.0333051, -0.012229, 0.0110322, -0.0457725, -0.000832209, -0.0202817,
        0.0327257, 0.0121308, 0.0155969, 0.0312091}},

      {{-0.0213783, 0.0350169, 0.000324794, 0.0276012, -0.0263374, -0.0371449,
        0.0446149, -0.0205474, 0.0103729, -0.0576349, -0.0150052, -0.0292043,
        0.0376827, 0.0136115, 0.0243435, 0.0354492}},

      {{-0.0189322, 0.0464512, -0.00251373, 0.0225745, -0.0308346, -0.0317124,
        0.0460407, -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193,
        0.0286833, 0.00824207, 0.0264887, 0.0305169}}};

  LSTMOpModel lstm(
      n_batch, n_input, n_cell, n_output,
      /*use_cifg=*/false, /*use_peephole=*/true,
      /*use_projection_weights=*/true,
      /*use_projection_bias=*/false, /*weight_type=*/TensorType_FLOAT32,
      model_has_legacy_20_inputs,
      /*is_layer_norm=*/false, /*asymmetric_quantize_inputs=*/false,
      input_to_input_weights_, input_to_forget_weights_, input_to_cell_weights_,
      input_to_output_weights_, recurrent_to_input_weights_,
      recurrent_to_forget_weights_, recurrent_to_cell_weights_,
      recurrent_to_output_weights_, cell_to_input_weights_,
      cell_to_forget_weights_, cell_to_output_weights_, input_gate_bias_,
      forget_gate_bias_, cell_gate_bias_, output_gate_bias_,
      projection_weights_, {}, input_layer_norm_coefficients_,
      forget_layer_norm_coefficients_, cell_layer_norm_coefficients_,
      output_layer_norm_coefficients_);

  VerifyGoldens(&lstm, 0.00001f);
}

TEST_F(LstmOpTest, NoCifg_Peephole_Projection_LayerNorm) {
  const int n_batch = 1;
  const int n_input = 5;
  const int n_cell = 4;
  const int n_output = 3;

  input_to_input_weights_ = {0.5,  0.6,  0.7,  -0.8, -0.9, 0.1,  0.2,
                             0.3,  -0.4, 0.5,  -0.8, 0.7,  -0.6, 0.5,
                             -0.4, -0.5, -0.4, -0.3, -0.2, -0.1};

  input_to_forget_weights_ = {-0.6, -0.1, 0.3,  0.2,  0.9,  -0.5, -0.2,
                              -0.4, 0.3,  -0.8, -0.4, 0.3,  -0.5, -0.4,
                              -0.6, 0.3,  -0.4, -0.6, -0.5, -0.5};

  input_to_cell_weights_ = {-0.4, -0.3, -0.2, -0.1, -0.5, 0.5,  -0.2,
                            -0.3, -0.2, -0.6, 0.6,  -0.1, -0.4, -0.3,
                            -0.7, 0.7,  -0.9, -0.5, 0.8,  0.6};

  input_to_output_weights_ = {-0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3,
                              -0.3, -0.8, -0.2, 0.6,  -0.2, 0.4,  -0.7,
                              -0.3, -0.5, 0.1,  0.5,  -0.6, -0.4};

  input_gate_bias_ = {0.03, 0.15, 0.22, 0.38};

  forget_gate_bias_ = {0.1, -0.3, -0.2, 0.1};

  cell_gate_bias_ = {-0.05, 0.72, 0.25, 0.08};

  output_gate_bias_ = {0.05, -0.01, 0.2, 0.1};

  recurrent_to_input_weights_ = {-0.2, -0.3, 0.4,  0.1,  -0.5, 0.9,
                                 -0.2, -0.3, -0.7, 0.05, -0.2, -0.6};

  recurrent_to_cell_weights_ = {-0.3, 0.2, 0.1, -0.3, 0.8,  -0.08,
                                -0.2, 0.3, 0.8, -0.6, -0.1, 0.2};

  recurrent_to_forget_weights_ = {-0.5, -0.3, -0.5, -0.2, 0.6, 0.4,
                                  0.9,  0.3,  -0.1, 0.2,  0.5, 0.2};

  recurrent_to_output_weights_ = {0.3,  -0.1, 0.1,  -0.2, -0.5, -0.7,
                                  -0.2, -0.6, -0.1, -0.4, -0.7, -0.2};

  cell_to_input_weights_ = {0.05, 0.1, 0.25, 0.15};

  cell_to_forget_weights_ = {-0.02, -0.15, -0.25, -0.03};

  cell_to_output_weights_ = {0.1, -0.1, -0.5, 0.05};

  input_layer_norm_coefficients_ = {0.1, 0.2, 0.3, 0.5};
  forget_layer_norm_coefficients_ = {0.2, 0.2, 0.4, 0.3};
  cell_layer_norm_coefficients_ = {0.7, 0.2, 0.3, 0.8};
  output_layer_norm_coefficients_ = {0.6, 0.2, 0.2, 0.5};

  projection_weights_ = {-0.1, 0.2,  0.01, -0.2, 0.1,  0.5,
                         0.3,  0.08, 0.07, 0.2,  -0.4, 0.2};

  lstm_input_ = {
      {{0.7, 0.8, 0.1, 0.2, 0.3}},
      {{0.8, 0.1, 0.2, 0.4, 0.5}},
      {{0.2, 0.7, 0.7, 0.1, 0.7}},
  };

  lstm_golden_output_ = {{{0.0244077, 0.128027, -0.00170918}},
                         {{0.0137642, 0.140751, 0.0395835}},
                         {{-0.00459231, 0.155278, 0.0837377}}};

  LSTMOpModel lstm(
      n_batch, n_input, n_cell, n_output,
      /*use_cifg=*/false, /*use_peephole=*/true,
      /*use_projection_weights=*/true,
      /*use_projection_bias=*/false, /*weight_type=*/TensorType_FLOAT32,
      /*model_has_legacy_20_inputs=*/false,
      /*is_layer_norm=*/true, /*asymmetric_quantize_inputs=*/false,
      input_to_input_weights_, input_to_forget_weights_, input_to_cell_weights_,
      input_to_output_weights_, recurrent_to_input_weights_,
      recurrent_to_forget_weights_, recurrent_to_cell_weights_,
      recurrent_to_output_weights_, cell_to_input_weights_,
      cell_to_forget_weights_, cell_to_output_weights_, input_gate_bias_,
      forget_gate_bias_, cell_gate_bias_, output_gate_bias_,
      projection_weights_, {}, input_layer_norm_coefficients_,
      forget_layer_norm_coefficients_, cell_layer_norm_coefficients_,
      output_layer_norm_coefficients_);

  VerifyGoldens(&lstm, 0.00001f);
}

TEST_F(LstmOpTest, Cifg_Peephole_Projection_LayerNorm) {
  const int n_batch = 1;
  const int n_input = 5;
  const int n_cell = 4;
  const int n_output = 3;

  input_to_forget_weights_ = {-0.6, -0.1, 0.3,  0.2,  0.9,  -0.5, -0.2,
                              -0.4, 0.3,  -0.8, -0.4, 0.3,  -0.5, -0.4,
                              -0.6, 0.3,  -0.4, -0.6, -0.5, -0.5};
  input_to_cell_weights_ = {-0.4, -0.3, -0.2, -0.1, -0.5, 0.5,  -0.2,
                            -0.3, -0.2, -0.6, 0.6,  -0.1, -0.4, -0.3,
                            -0.7, 0.7,  -0.9, -0.5, 0.8,  0.6};
  input_to_output_weights_ = {-0.8, -0.4, -0.2, -0.9, -0.1, -0.7, 0.3,
                              -0.3, -0.8, -0.2, 0.6,  -0.2, 0.4,  -0.7,
                              -0.3, -0.5, 0.1,  0.5,  -0.6, -0.4};

  forget_gate_bias_ = {0.1, -0.3, -0.2, 0.1};
  cell_gate_bias_ = {-0.05, 0.72, 0.25, 0.08};
  output_gate_bias_ = {0.05, -0.01, 0.2, 0.1};

  recurrent_to_cell_weights_ = {-0.3, 0.2, 0.1, -0.3, 0.8,  -0.08,
                                -0.2, 0.3, 0.8, -0.6, -0.1, 0.2};
  recurrent_to_forget_weights_ = {-0.5, -0.3, -0.5, -0.2, 0.6, 0.4,
                                  0.9,  0.3,  -0.1, 0.2,  0.5, 0.2};
  recurrent_to_output_weights_ = {0.3,  -0.1, 0.1,  -0.2, -0.5, -0.7,
                                  -0.2, -0.6, -0.1, -0.4, -0.7, -0.2};

  cell_to_forget_weights_ = {-0.02, -0.15, -0.25, -0.03};
  cell_to_output_weights_ = {0.1, -0.1, -0.5, 0.05};

  forget_layer_norm_coefficients_ = {0.2, 0.2, 0.4, 0.3};
  cell_layer_norm_coefficients_ = {0.7, 0.2, 0.3, 0.8};
  output_layer_norm_coefficients_ = {0.6, 0.2, 0.2, 0.5};
  projection_weights_ = {-0.1, 0.2,  0.01, -0.2, 0.1,  0.5,
                         0.3,  0.08, 0.07, 0.2,  -0.4, 0.2};

  lstm_input_ = {{{0.7, 0.8, 0.1, 0.2, 0.3}},
                 {{0.8, 0.1, 0.2, 0.4, 0.5}},
                 {{0.2, 0.7, 0.7, 0.1, 0.7}}};
  lstm_golden_output_ = {{{0.02129706, 0.140816242, 0.0112733059}},
                         {{0.0132302344, 0.152308047, 0.0346313119}},
                         {{-0.0123688057, 0.165790111, 0.0893077999}}};

  LSTMOpModel lstm(
      n_batch, n_input, n_cell, n_output,
      /*use_cifg=*/true, /*use_peephole=*/true,
      /*use_projection_weights=*/true,
      /*use_projection_bias=*/false, /*weight_type=*/TensorType_FLOAT32,
      /*model_has_legacy_20_inputs=*/false,
      /*is_layer_norm=*/true, /*asymmetric_quantize_inputs=*/false,
      input_to_input_weights_, input_to_forget_weights_, input_to_cell_weights_,
      input_to_output_weights_, recurrent_to_input_weights_,
      recurrent_to_forget_weights_, recurrent_to_cell_weights_,
      recurrent_to_output_weights_, cell_to_input_weights_,
      cell_to_forget_weights_, cell_to_output_weights_, input_gate_bias_,
      forget_gate_bias_, cell_gate_bias_, output_gate_bias_,
      projection_weights_, {}, input_layer_norm_coefficients_,
      forget_layer_norm_coefficients_, cell_layer_norm_coefficients_,
      output_layer_norm_coefficients_);

  VerifyGoldens(&lstm, 0.00001f);
}

#ifdef GTEST_HAS_DEATH_TEST
TEST_F(LstmOpTest, InvalidTypes) {
  const int n_batch = 1;
  const int n_input = 2;
  const int n_cell = 4;
  const int n_output = 4;

  EXPECT_DEATH(
      LSTMOpModel lstm(
          n_batch, n_input, n_cell, n_output,
          /*use_cifg=*/false, /*use_peephole=*/false,
          /*use_projection_weights=*/false,
          /*use_projection_bias=*/false,
          /*weight_type=*/TensorType_INT32,
          /*model_has_legacy_20_inputs=*/true,
          /*is_layer_norm=*/false,
          /*asymmetric_quantize_inputs=*/false,
          /*input_to_input_weights=*/{}, /*input_to_forget_weights=*/{},
          /*input_to_cell_weights=*/{}, /*input_to_output_weights=*/{},
          /*recurrent_to_input_weights=*/{},
          /*recurrent_to_forget_weights=*/{}, /*recurrent_to_cell_weights=*/{},
          /*recurrent_to_output_weights=*/{}, /*cell_to_input_weights=*/{},
          /*cell_to_forget_weights=*/{}, /*cell_to_output_weights=*/{},
          /*input_gate_bias=*/{}, /*forget_gate_bias=*/{},
          /*cell_gate_bias=*/{}, /*output_gate_bias=*/{},
          /*projection_weights=*/{}, /*projection_bias=*/{},
          /*input_layer_norm_coefficients=*/{},
          /*forget_layer_norm_coefficients=*/{},
          /*cell_layer_norm_coefficients=*/{},
          /*output_layer_norm_coefficients=*/{}),
      "");

  EXPECT_DEATH(
      LSTMOpModel lstm(
          n_batch, n_input, n_cell, n_output,
          /*use_cifg=*/false, /*use_peephole=*/false,
          /*use_projection_weights=*/false,
          /*use_projection_bias=*/false,
          /*weight_type=*/TensorType_COMPLEX64,
          /*model_has_legacy_20_inputs=*/true,
          /*is_layer_norm=*/false,
          /*asymmetric_quantize_inputs=*/false,
          /*input_to_input_weights=*/{}, /*input_to_forget_weights=*/{},
          /*input_to_cell_weights=*/{}, /*input_to_output_weights=*/{},
          /*recurrent_to_input_weights=*/{},
          /*recurrent_to_forget_weights=*/{}, /*recurrent_to_cell_weights=*/{},
          /*recurrent_to_output_weights=*/{}, /*cell_to_input_weights=*/{},
          /*cell_to_forget_weights=*/{}, /*cell_to_output_weights=*/{},
          /*input_gate_bias=*/{}, /*forget_gate_bias=*/{},
          /*cell_gate_bias=*/{}, /*output_gate_bias=*/{},
          /*projection_weights=*/{}, /*projection_bias=*/{},
          /*input_layer_norm_coefficients=*/{},
          /*forget_layer_norm_coefficients=*/{},
          /*cell_layer_norm_coefficients=*/{},
          /*output_layer_norm_coefficients=*/{}),
      "");
}
#endif

// Test parameter controls model_has_legacy_20_inputs in LSTMOpModel.
INSTANTIATE_TEST_SUITE_P(Parameterized, LstmOpTest, ::testing::Bool());

}  // namespace
}  // namespace tflite
